Pharmacogenomics of aromatase inhibitors in postmenopausal breast cancer and additional mechanisms of anastrozole action

A Meta-Analysis: Anti-Inflammatory Medicinal Plants for Age-Related Menopause-Like Symptoms and Psychological Problems in Breast Cancer and Healthy Perimenopausal Women

BACKGROUND

Medicinal plant supplements (MPS) have benefits in improving menopause-like symptoms (MLS).

OBJECTIVES

To investigate the effectiveness of MPS in reducing MLS in healthy women and those with breast cancer (BC).

SEARCH STRATEGY

PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang database and Chinese Scientific Journals Database were searched from the date of library construction until 30 January 2024.

SELECTION CRITERIA

Randomised controlled trials were selected that involved healthy perimenopausal women, BC patients treated with herbal medicines, and the effects of plant-based compounds on MLS.

DATA COLLECTION AND ANALYSIS

The review included 12 studies with 917 patients with BC and 15 studies with 2104 healthy perimenopausal women. The data were analysed using the Meta-mar tool.

MAIN RESULTS

MPS improved Kupperman's Index (KMI) and menopause rating scale (MRS) scores compared with the comparator. Patients with BC experienced a greater reduction in KMI and MRS because of treatment than healthy perimenopausal women. Patients with BC had a more significant reduction in KMI than women who received hormone therapy. In both healthy perimenopausal women and women with BC, MPS treatment resulted in significantly higher response rates and fewer psychological problems. Women with BC experienced a decrease in anxiety and insomnia by MPS. Network analysis showed that the response rate was the factor most associated with MPS use.

CONCLUSIONS

Anti-inflammatory MPS may assist women with BC or healthy perimenopausal women experience less MLS.

Molecular Subtypes and Mechanisms of Breast Cancer: Precision Medicine Approaches for Targeted Therapies

Breast cancer remains one of the most prevalent diseases worldwide, primarily affecting women. Its heterogeneous nature poses a significant challenge in the development of effective and targeted treatments. Molecular characterization has enabled breast cancer to be classified into four main subtypes: luminal A, luminal B, HER2-positive, and triple-negative breast cancer, based on hormone receptor expression and HER2 status. A deeper understanding of these molecular markers and their associated signaling pathways, such as MAPK and PI3K/AKT, is essential for improving prognosis and optimizing treatment strategies. Currently, several therapeutic agents are utilized in neoadjuvant and adjuvant therapies, often in combination with surgical interventions. However, emerging evidence highlights the growing challenge of drug resistance, which significantly limits the efficacy of existing treatments. Addressing this issue may require innovative approaches, including combination therapies and precision medicine strategies, tailored to the molecular profile of each patient. Therefore, a comprehensive understanding of the pathophysiologic mechanisms driving breast cancer progression and resistance is crucial for the development of advanced targeted therapies with greater precision and efficacy. This review aims to explore recent advancements in molecular research related to breast cancer subtypes and provide a critical analysis of current therapeutic approaches within the framework of precision medicine.

Plasma Proteomics and Metabolomics of Aromatase Inhibitors-Related Musculoskeletal Syndrome in Early Breast Cancer Patients

BACKGROUND

Aromatase inhibitors-related musculoskeletal syndrome (AIMSS) is a common side effect experienced by early breast cancer patients undergoing endocrine therapy. This condition can result in medication discontinuation and a diminished quality of life. The objective of this study was to characterize AIMSS, investigate its pathogenesis, and identify potential biomarkers at both the protein and metabolic levels.

METHODS

We collected peripheral blood samples from 60 women diagnosed with breast cancer undergoing aromatase inhibitor therapy, of whom 30 had AIMSS and 30 did not. The samples were analyzed using four-dimensional data-independent acquisition (DIA)-based proteomics and untargeted metabolomics, employing liquid chromatography-mass spectrometry (LC-MS) on the latest platform.

RESULTS

The mean age of participants was 49.2 (11.3) years in the AIMSS group and 50.1 (11.5) years in the non-AIMSS group. There were no statistically significant differences between the two groups in terms of age, BMI, education level, clinical stage, and treatment. In total, we identified 3473 proteins and 1247 metabolites in the samples. The chemokine signaling pathway (p = 0.015), cytokine-cytokine receptor interaction (p = 0.015), complement and coagulation cascades (p = 0.004), neuroactive ligand-receptor interaction (p = 0.004), and the estrogen signaling pathway (p = 0.004) were significant enriched in differentially expressed proteins (DEPs). GnRH secretion (p < 0.001), sphingolipid signaling pathways (p < 0.001), endocrine resistance (p < 0.001), the estrogen signaling pathway (p = 0.001), endocrine and other factor-regulated calcium reabsorption (p = 0.001), dopaminergic synapse (p = 0.003), regulation of lipolysis in adipocytes (p = 0.004), biosynthesis of cofactors (p = 0.004), thyroid hormone synthesis (p = 0.008), aldosterone synthesis and secretion (p = 0.001), taurine and hypotaurine metabolism (p = 0.011), ovarian steroidogenesis (p = 0.011), and the cAMP signaling pathway (p = 0.011) were significantly enriched in differentially expressed metabolites (DEMs). Complement C3 (p = 0.004), platelet factor 4 (p = 0.015), KRT10 (p = 0.004), KRT14 (p = 0.004), beta-estradiol (p = 0.019), testosterone (p = 0.023), sphingosine (p < 0.001), and 1-stearoyl-2-arachidonoyl-sn-glycerol (p = 0.039) could be the monitoring and therapeutic targets for AIMSS.

CONCLUSIONS

This study offered new insights into the mechanisms underlying musculoskeletal symptoms associated with aromatase inhibitors. It also highlighted potential biomarkers for predicting and addressing these symptoms in breast cancer patients, paving the way for improved intervention strategies.

Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer

The role of germline genetics in adjuvant aromatase inhibitor (AI) treatment efficacy in ER-positive breast cancer is poorly understood. We employed a two-stage candidate gene approach to examine associations between survival endpoints and common germline variants in 753 endocrine resistance-related genes. For a discovery cohort, we screened the Breast Cancer Association Consortium database (n ≥ 90,000 cases) and retrieved 2789 AI-treated patients. Cox model-based analysis revealed 125 variants associated with overall, distant relapse-free, and relapse-free survival (p-value ≤ 1E-04). In validation analysis using five independent cohorts (n = 8857), none of the six selected candidates representing major linkage blocks at CELA2B/CASP9, NR1I2/GSK3B, LRP1B, and MIR143HG (CARMN) were validated. We discuss potential reasons for the failed validation and replication of published findings, including study/treatment heterogeneity and other limitations inherent to genomic treatment outcome studies. For the future, we envision prospective longitudinal studies with sufficiently long follow-up and endpoints that reflect the dynamic nature of endocrine resistance.

Imlunestrant Is an Oral, Brain-Penetrant Selective Estrogen Receptor Degrader with Potent Antitumor Activity in ESR1 Wild-Type and Mutant Breast Cancer

Targeting of the estrogen receptor (ER) by antiestrogens is the standard of care for patients with ER+ HER2- advanced/metastatic breast cancer. Although antiestrogens that degrade ERα (fulvestrant) or block estrogen production (aromatase inhibitors) have improved patient outcomes, clinically important challenges remain related to drug administration, limited bioavailability, lack of brain exposure, and acquired resistance due to ESR1 mutations. These limitations indicate a need for more robust ER-targeted therapies. Here, we discovered and characterized imlunestrant, a next-generation potent, brain-penetrant oral selective ER degrader. Imlunestrant degraded ERα and decreased ERα-mediated gene expression both in vitro and in vivo. Cell proliferation and tumor growth in ESR1 wild-type (WT) and mutant models were significantly inhibited by imlunestrant. Combining imlunestrant with abemaciclib (CDK4/6 inhibitor), alpelisib (PI3K inhibitor), or everolimus (mTOR inhibitor) further enhanced tumor growth inhibition, regardless of ESR1 mutational status. In an ER+ breast cancer intracranial tumor model, imlunestrant prolonged survival compared with vehicle or alternative selective ER degrader therapies. Together, these findings support the potential of imlunestrant to degrade ERα and suppress the growth of ESR1-WT and mutant breast cancer, including brain metastatic tumors. Significance: Imlunestrant, a next-generation, brain-penetrant oral ERα degrader, displays potent activity in ESR1 wild-type and mutant breast cancer, enhances combination activity with standard-of-care agents, and inhibits growth of ER+ intracranial tumors.

CYP19A1 regulates chemoresistance in colorectal cancer through modulation of estrogen biosynthesis and mitochondrial function

Chemoresistance remains a major challenge in the effective treatment of colorectal cancer (CRC), contributing to poor patient outcomes. While the molecular mechanisms underlying chemoresistance are complex and multifaceted, emerging evidence suggests that altered mitochondrial function and hormone signaling play crucial roles. In this study, we investigated the role of CYP19A1, a key enzyme in estrogen biosynthesis, in regulating chemoresistance in CRC. Using a combination of in vitro functional assays, transcriptomic analysis, and clinical data mining, we demonstrate that CYP19A1 expression is significantly upregulated in CRC cells and patient-derived samples compared to normal controls. Mechanistically, we found that CYP19A1 regulates chemoresistance through modulation of mitochondrial function and complex I activity, which is mediated by CYP19A1-dependent estrogen biosynthesis. Notably, targeted inhibition of CYP19A1 and complex I using specific inhibitors effectively reversed the chemoresistance of CRC cells to chemotherapeutic drugs. Moreover, analysis of the TCGA CRC dataset revealed that high CYP19A1 expression correlates with poor overall survival in chemotherapy-treated patients. Taken together, our findings uncover a novel role for CYP19A1 in regulating chemoresistance in CRC through modulation of mitochondrial function and estrogen signaling, and highlight the potential of targeting the CYP19A1/estrogen/complex I axis as a therapeutic strategy to overcome chemoresistance and improve patient outcomes.

Oral nano-formulations for endocrine therapy of endometrioid adenocarcinomas

Endometrial cancer is one of the three major malignant tumors of the reproductive system that threaten women's lives and health. The incidence of this disease is on the rise globally. Most cases of endometrial cancer comprise endometrioid adenocarcinomas, whose treatment is challenged by factors such as their high recurrence rate and the need to preserve fertility among young patients. Thus, oral endocrine therapy has become the main treatment modality. The main drugs used in oral endocrine therapy are progestins, selective estrogen receptor antagonists, and aromatase inhibitors. However, their clinical use is hindered by their low solubility and low oral utilization. The rapid development of nanotechnology allows the combination of these drugs with oral nano-formulations to create a good carrier. Such nanocarriers, including nanospheres, nanocapsules, and micelles can protect the drug against clearance and increase the site specificity of drug delivery. This paper reviews the pathogenesis of endometrioid endometrial cancer (EEC) and oral nano-formulations for endocrine therapy.

Androgen receptor-mediated pharmacogenomic expression quantitative trait loci: implications for breast cancer response to AR-targeting therapy

BACKGROUND

Endocrine therapy is the most important treatment modality of breast cancer patients whose tumors express the estrogen receptor α (ERα). The androgen receptor (AR) is also expressed in the vast majority (80-90%) of ERα-positive tumors. AR-targeting drugs are not used in clinical practice, but have been evaluated in multiple trials and preclinical studies.

METHODS

We performed a genome-wide study to identify hormone/drug-induced single nucleotide polymorphism (SNP) genotype - dependent gene-expression, known as PGx-eQTL, mediated by either an AR agonist (dihydrotestosterone) or a partial antagonist (enzalutamide), utilizing a previously well characterized lymphoblastic cell line panel. The association of the identified SNPs-gene pairs with breast cancer phenotypes were then examined using three genome-wide association (GWAS) studies that we have published and other studies from the GWAS catalog.

RESULTS

We identified 13 DHT-mediated PGx-eQTL loci and 23 Enz-mediated PGx-eQTL loci that were associated with breast cancer outcomes post ER antagonist or aromatase inhibitors (AI) treatment, or with pharmacodynamic (PD) effects of AIs. An additional 30 loci were found to be associated with cancer risk and sex-hormone binding globulin levels. The top loci involved the genes IDH2 and TMEM9, the expression of which were suppressed by DHT in a PGx-eQTL SNP genotype-dependent manner. Both of these genes were overexpressed in breast cancer and were associated with a poorer prognosis. Therefore, suppression of these genes by AR agonists may benefit patients with minor allele genotypes for these SNPs.

CONCLUSIONS

We identified AR-related PGx-eQTL SNP-gene pairs that were associated with risks, outcomes and PD effects of endocrine therapy that may provide potential biomarkers for individualized treatment of breast cancer.

Role of ERα and Aromatase in Juvenile Gigantomastia

CONTEXT

Approximately 150 patients with juvenile gigantomastia have been reported in the literature but the underlying biologic mechanisms remain unknown.

OBJECTIVE

To conduct extensive clinical, biochemical, immunochemical, and genetic studies in 3 patients with juvenile gigantomastia to determine causative biologic factors.

METHODS

We examined clinical effects of estrogen by blockading estrogen synthesis or its action. Breast tissue aromatase expression and activity were quantitated in 1 patient and 5 controls. Other biochemical markers, including estrogen receptor α (ERα), cyclin D1 and E, p-RB, p-MAPK, p-AKT, BCL-2, EGF-R, IGF-IR β, and p-EGFR were assayed by Western blot. Immunohistochemical analyses for aromatase, ERα and β, PgR, Ki67, sulfotransferase, estrone sulfatase, and 17βHD were performed in all 3 patients. The entire genomes of the mother, father, and patient in the 3 families were sequenced.

RESULTS

Blockade of estrogen synthesis or action in patients resulted in demonstrable clinical effects. Biochemical studies on fresh frozen tissue revealed no differences between patients and controls, presumably due to tissue dilution from the large proportion of stroma. However, immunohistochemical analysis of ductal breast cells in the 3 patients revealed a high percent of ERα (64.1% ± 7.8% vs reference women 9.6%, range 2.3-15%); aromatase score of 4 (76%-100% of cells positive vs 30.4% ± 5.6%); PgR (69.5% ± 15.2% vs 6.0%, range 2.7%-11.9%) and Ki67 (23.7% ± 0.54% vs 4.2%). Genetic studies were inconclusive although some intriguing variants were identified.

CONCLUSION

The data implicate an important biologic role for ERα to increase tissue sensitivity to estrogen and aromatase to enhance local tissue production as biologic factors involved in juvenile gigantomastia.

Drug repurposing for personalized medicine

Personalized medicine has emerged as a revolutionary approach to healthcare in the 21st century. By understanding a patient's unique genetic and biological characteristics, it aims to tailor treatments specifically to the individual. This approach takes into account factors such as an individual's lifestyle, genetic makeup, and environmental factors to provide targeted therapies that have the potential to be more effective and lower the risk of side reactions or ineffective treatments. It is a paradigm shift from the traditional "one size fits all" approach in medicine, where patients with similar symptoms or diagnoses receive the same standard treatments regardless of their differences. It leads to improved clinical outcomes and more efficient use of healthcare resources. Drug repurposing is a strategy that uses existing drugs for new indications and aims to take advantage of the known safety profiles, pharmacokinetics, and mechanisms of action of these drugs to accelerate the development process. Precision medicine may undergo a revolutionary change as a result, enabling the rapid development of novel treatment plans utilizing drugs that traditional methods would not otherwise link to. In this chapter, we have focused on a few strategies wherein drug repurposing has shown great success for precision medicine. The approach is particularly useful in oncology as there are many variations induced in the genetic material of cancer patients, so tailored treatment approaches go a long way. We have discussed the cases of breast cancer, glioblastoma and hepatocellular carcinoma. Other than that, we have also looked at drug repurposing approaches in anxiety disorders and COVID-19.

Evaluating flavonoids as potential aromatase inhibitors for breast cancer treatment: In vitro studies and in silico predictions

Aromatase inhibitors are commonly employed in the treatment of hormone-dependent breast cancers, and flavonoids have emerged as a promising alternative to existing drug classes with unfavorable side effects. In this study, we conducted in vitro investigations into CYP19A1 (aromatase) inhibitory potential of 14 flavonoids, including pinocembrin, sakuranetin, eriodictyol, liquiritigenin, naringenin, hesperetin, flavanone, baicalein, chrysin, nobiletin, luteolin, sinensetin, tricin, and primuletin. Flavonoids displaying inhibitory activity were further assessed using in silico tools, such as molecular docking to predict binding affinities, as well as SwissADME, admetSAR, and QED (Quantitative Estimate of Drug-likeness) for drug-likeness prediction. Flavonoids with IC50 values less than 10 μM, pinocembrin, eriodictyol, naringenin, liquirtigenin, sakuranetin, and chrysin, exhibited favorable physicochemical properties and ADME profiles, suggesting their potential for development as novel flavonoid-based aromatase inhibitors. This study would provide valuable insights for the development of flavonoid-based aromatase inhibitors for the treatment of breast cancer.

Efficacy and safety of traditional Chinese medicine in the treatment of menopause-like syndrome for breast cancer survivors: a systematic review and meta-analysis

BACKGROUND

In recent years, breast cancer (BC) incidence and mortality have been the highest in females. Menopause-like syndrome (MLS), arising from hypoestrogenism caused by endocrine therapy, significantly affects the quality of life for females. Traditional Chinese Medicine (TCM) has advantages in ameliorating MLS, but the efficacy of TCM in patients with BC has not been systematically evaluated.

METHODS

A comprehensive search was performed on PubMed, Web of Science, Embase, Ovid, Cochrane Library, China National Knowledge Infrastructure, Wanfang database, Chinese Scientific Journals Database, and Clinical Trial Registry from inception to September 4, 2023. The Cochrane Risk of Bias assessment tool was used for the quality evaluation of the randomized controlled trials (RCTs). Review Manager 5.4 software was used for statistical analysis, and the Grading of Recommendations Assessment, Development, and Evaluation was used for quality evaluation of the synthesized evidence.

RESULTS

This review included 42 studies involving 3112 female patients with BC. The results showed that the TCM group was better at decreasing the Kupperman Menopausal Index (KMI) scores (standardized MD, SMD = - 1.84, 95% confidence interval, CI [- 2.21--1.46], Z = 9.63, P < 0.00001). Regarding the main symptoms of MLS, the TCM groups could significantly decrease the scores of hot flashes and night sweats (SMD = - 0.68, 95% CI [- 1.1--0.27], Z = 3.24, P = 0.001), paraesthesia (SMD = - 0.48, 95% CI [- 0.74--0.21], Z = 3.53, P = 0.0004), osteoarthralgia (SMD = - 0.41, 95% CI [- 0.6-0.21], Z = 4.09, P < 0.0001), anxiety (MD = - 0.85, 95% CI [- 1.13, - 0.58], Z = 6.08, P < 0.00001) and insomnia (MD = - 0.61, 95% CI [- 0.8, - 0.43], Z = 6.51, P < 0.00001). TCM can effectively improve the symptoms of MLS in patients with BC. Moreover, TCM could improve the objective response rate (ORR) by 50% (RR = 1.5, 95% CI [1.37-1.64], Z = 9.01, P < 0.00001). Follicle-stimulating hormone (FSH) and oestradiol (E2) had no significant difference compared with the control group (p = 0.81 and p = 0.87), and luteinizing hormone (LH) in the TCM group decreased significantly (MD = - 0.99, 95% CI [- 1.38, - 0.5], Z = 5.01, P < 0.00001). This means that the use of TCM does not negatively affect endocrine therapy and may even have a synergistic effect. The incidence of adverse events (AEs) was lower in the TCM groups than in the control groups.

CONCLUSIONS

The meta-analysis stated that TCM could better improve the MLS of patients, alleviate related symptoms, and did not increase adverse drug reactions in BC survivors. This review brings more attention to MLS, and the present findings shed light on the potential applications of TCM in the treatment of MLS in BC survivors.

The inhibition of steroid hormones determines the fate of IPC-366 tumor cells, highlighting the crucial role of androgen production in tumor processes

Inflammatory mammary cancer (IMC) is a disease that affects female dogs. It is characterized by poor treatment options and no efficient targets. However, anti-androgenic and anti-estrogenic therapies could be effective because IMC has a great endocrine influence, affecting tumor progression. IPC-366 is a triple negative IMC cell line that has been postulated as a useful model to study this disease. Therefore, the aim of this study was to inhibit steroid hormones production at different points of the steroid pathway in order to determine its effect in cell viability and migration in vitro and tumor growth in vivo. For this purpose, Dutasteride (anti-5αReductase), Anastrozole (anti-aromatase) and ASP9521 (anti-17βHSD) and their combinations have been used. Results revealed that this cell line is positive to estrogen receptor β (ERβ) and androgen receptor (AR) and endocrine therapies reduce cell viability. Our results enforced the hypothesis that estrogens promote cell viability and migration in vitro due to the function of E1SO4 as an estrogen reservoir for E2 production that promotes the IMC cells proliferation. Also, an increase in androgen secretion was associated with a reduction in cell viability. Finally, in vivo assays showed large tumor reduction. Hormone assays determined that high estrogen levels and the reduction of androgen levels promote tumor growth in Balb/SCID IMC mice. In conclusion, estrogen levels reduction may be associated with a good prognosis. Also, activation of AR by increasing androgen production could result in effective therapy for IMC because their anti-proliferative effect.

Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

Polygenic Pharmacogenomic Markers as Predictors of Toxicity Phenotypes in the Treatment of Acute Lymphoblastic Leukemia: A Single-Center Study

PURPOSE

Acute lymphoblastic leukemia (ALL) is the most prevalent cause of childhood cancer and requires a long course of therapy consisting of three primary phases with interval intensification blocks. Although these phases are necessary to achieve remission, the primary chemotherapeutic agents have potentially serious toxicities, which may lead to delays or discontinuations of therapy. The purpose of this study was to perform a comprehensive pharmacogenomic evaluation of common antileukemic agents and develop a polygenic toxicity risk score predictive of the most common toxicities observed during ALL treatment.

METHODS

This cross-sectional study included 75 patients with pediatric ALL treated between 2012 and 2020 at the University of Florida. Toxicity data were collected within 100 days of initiation of therapy using CTCAE v4.0 for toxicity grading. For pharmacogenomic evaluation, single-nucleotide polymorphisms (SNPs) and genes were selected from previous reports or PharmGKB database. 116 unique SNPs were evaluated for incidence of various toxicities. A multivariable multi-SNP modeling for up to 3-SNP combination was performed to develop a polygenic toxicity risk score of prognostic value.

RESULTS

We identified several SNPs predictive of toxicity phenotypes in univariate analysis. Further multivariable SNP-SNP combination analysis suggest that susceptibility to chemotherapy-induced toxicities is likely multigenic in nature. For 3-SNPscore models, patients with high scores experienced increased risk of GI (P = 2.07E-05, 3 SNPs: TYMS-rs151264360/FPGS-rs1544105/GSTM1-GSTM5-rs3754446), neurologic (P = .0005, 3 SNPs: DCTD-rs6829021/SLC28A3-rs17343066/CTPS1-rs12067645), endocrine (P = 4.77E-08, 3 SNPs: AKR1C3-rs1937840/TYMS-rs2853539/CTH-rs648743), and heme toxicities (P = .053, 3 SNPs: CYP3A5-rs776746/ABCB1-rs4148737/CTPS1-rs12067645).

CONCLUSION

Our results imply that instead of a single-SNP approach, SNP-SNP combinations in multiple genes in drug pathways increases the robustness of prediction of toxicity. These results further provide promising SNP models that can help establish clinically relevant biomarkers allowing for greater individualization of cancer therapy to maximize efficacy and minimize toxicity for each patient.

The integrative bioinformatics approaches to predict the xanthohumol as anti-breast cancer molecule: Targeting cancer cells signaling PI3K and AKT kinase pathway

BACKGROUND

Breast cancer is the most common type of cancer in women, and vast research is being conducted throughout the world for the treatment of this malignancy by natural products using various computational approaches. Xanthohumol, a prenylated flavonoid, is known for its anticancer activity; however, the mechanism behind its action is still in the preliminary stage.

METHODS

The current study aimed to analyze the efficacy of xanthohumol compared to the currently available anticancer drugs targeting phosphoinositide-3-kinase (PI3K), serine/threonine kinase (AKT) receptors, and human epidermal growth factor receptor 2 (HER2) for breast cancer treatment through in silico analysis.

RESULTS

The result revealed that the target compound showed significant binding affinity to targets within the PI3K, AKT, and HER2 signaling pathways with a binding energy of -7.5, -7.9, and -7.9 kcal/mol, respectively. Further prediction studies were then made concerning this compound's absorption, distribution, metabolism, and excretion (ADME) as well as drug-likeness properties, resulting in its oral bioavailability with only a single violation of Lipinski's rule of five.

CONCLUSIONS

The finding revealed the ability of xanthohumol to bind with multiple cancer cell signaling molecules including PI3K, AKT kinase, and HER2. The current novel study opened the door to advancing research into the management and treatment of breast cancer.

Tumor organoids: applications in cancer modeling and potentials in precision medicine

Cancer is a top-ranked life-threatening disease with intratumor heterogeneity. Tumor heterogeneity is associated with metastasis, relapse, and therapy resistance. These factors contribute to treatment failure and an unfavorable prognosis. Personalized tumor models faithfully capturing the tumor heterogeneity of individual patients are urgently needed for precision medicine. Advances in stem cell culture have given rise to powerful organoid technology for the generation of in vitro three-dimensional tissues that have been shown to more accurately recapitulate the structures, specific functions, molecular characteristics, genomic alterations, expression profiles, and tumor microenvironment of primary tumors. Tumoroids in vitro serve as an important component of the pipeline for the discovery of potential therapeutic targets and the identification of novel compounds. In this review, we will summarize recent advances in tumoroid cultures as an excellent tool for accurate cancer modeling. Additionally, vascularization and immune microenvironment modeling based on organoid technology will also be described. Furthermore, we will summarize the great potential of tumor organoids in predicting the therapeutic response, investigating resistance-related mechanisms, optimizing treatment strategies, and exploring potential therapies. In addition, the bottlenecks and challenges of current tumoroids will also be discussed in this review.

Drug Repurposing in Cancer Therapy: Influence of Patient’s Genetic Background in Breast Cancer Treatment

Cancer is among the leading causes of death worldwide and it is estimated that in 2040 more than 29 million people will be diagnosed with some type of cancer. The most prevalent type of cancer in women, worldwide, is breast cancer, a type of cancer associated with a huge death rate. This high mortality is mainly a consequence of the development of drug resistance, which is one of the major challenges to overcome in breast cancer treatment. As a result, research has been focused on finding novel therapeutical weapons, specifically ones that allow for a personalized treatment, based on patients’ characteristics. Although the scientific community has been concerned about guaranteeing the quality of life of cancer patients, researchers are also aware of the increasing costs related to cancer treatment, and efforts have been made to find alternatives to the development of new drugs. The development of new drugs presents some disadvantages as it is a multistep process that is time- and money-consuming, involving clinical trials that commonly fail in the initial phases. A strategy to overcome these disadvantages is drug repurposing. In this review, we focused on describing potential repurposed drugs in the therapy of breast cancer, considering their pharmacogenomic profile, to assess the relationship between patients’ genetic variations and their response to a certain therapy. This review supports the need for the development of further fundamental studies in this area, in order to investigate and expand the knowledge of the currently used and novel potential drugs to treat breast cancer. Future clinical trials should focus on developing strategies to group cancer patients according to their clinical and biological similarities and to discover new potential targets, to enable cancer therapy to be more effective and personalized.

Anastrozole Regulates Fatty Acid Synthase in Breast Cancer

Our previous matched case-control study of postmenopausal women with resected early-stage breast cancer revealed that only anastrozole, but not exemestane or letrozole, showed a significant association between the 6-month estrogen concentrations and risk of breast cancer. Anastrozole, but not exemestane or letrozole, is a ligand for estrogen receptor α. The mechanisms of endocrine resistance are heterogenous and with the new mechanism of anastrozole, we have found that treatment of anastrozole maintains fatty acid synthase (FASN) protein level by limiting the ubiquitin-mediated FASN degradation, leading to increased breast cancer cell growth. Mechanistically, anastrozole decreases the guided entry of tail-anchored proteins factor 4 (GET4) expression, resulting in decreased BCL2-associated athanogene cochaperone 6 (BAG6) complex activity, which in turn, prevents RNF126-mediated degradation of FASN. Increased FASN protein level can induce a negative feedback loop mediated by the MAPK pathway. High levels of FASN are associated with poor outcome only in patients with anastrozole-treated breast cancer, but not in patients treated with exemestane or letrozole. Repressing FASN causes regression of breast cancer cell growth. The anastrozole-FASN signaling pathway is eminently targetable in endocrine-resistant breast cancer.

Interaction Between SNP Genotype and Efficacy of Anastrozole and Exemestane in Early-Stage Breast Cancer

Aromatase inhibitors (AIs) are the treatment of choice for hormone receptor-positive early breast cancer in postmenopausal women. None of the third-generation AIs are superior to the others in terms of efficacy. We attempted to identify genetic factors that could differentiate between the effectiveness of adjuvant anastrozole and exemestane by examining single-nucleotide polymorphism (SNP)-treatment interaction in 4,465 patients. A group of SNPs were found to be differentially associated between anastrozole and exemestane regarding outcomes. However, they showed no association with outcome in the combined analysis. We followed up common SNPs near LY75 and GPR160 that could differentiate anastrozole from exemestane efficacy. LY75 and GPR160 participate in epithelial-to-mesenchymal transition and growth pathways, in both cases with SNP-dependent variation in regulation. Collectively, these studies identified SNPs that differentiate the efficacy of anastrozole and exemestane and they suggest additional genetic biomarkers for possible use in selecting an AI for a given patient.

Genomewide Association Studies in Pharmacogenomics

The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.

DDRS: Detection of drug response SNPs specifically in patients receiving drug treatment

Detecting SNPs associated with drug efficacy or toxicity is helpful to facilitate personalized medicine. Previous studies usually find SNPs associated with clinical outcome only in patients received a specific treatment. However, without information from patients without drug treatment, it is possible that the detected SNPs are associated with patients' clinical outcome even without drug treatment. Here we aimed to detect drug response SNPs based on data from patients with and without drug treatment through combing the cox proportional-hazards model and pairwise Kaplan-Meier survival analysis. A pipeline named Detection of Drug Response SNPs (DDRS) was built and applied to TCGA breast cancer data including 363 patients with doxorubicin treatment and 321 patients without any drug treatment. We identified 548 doxorubicin associated SNPs. Drug response score derived from these SNPs were associated with drug-resistant level (indicated by IC₅₀) of breast cancer cell lines. Enrichment analyses showed that these SNPs were enriched in active epigenetic regulation markers (e.g., H3K27ac). Compared with random genes, the cis-eQTL genes of these SNPs had a shorter protein-protein interaction distance to doxorubicin associated genes. In addition, linear discriminant analysis showed that the eQTL gene expression levels could be used to predict clinical outcome for patients with doxorubicin treatment (AUC = 0.738). Specifically, we identified rs2817101 as a drug response SNP for doxorubicin treatment. Higher expression level of its cis-eQTL gene GSTA1 is associated with poorer survival. This approach can also be applied to identify new drug associated SNPs in other cancers.